Mobile Sensor Networks and Control: Adaptive Sampling of Spatiotemporal Processes

The control of mobile sensor networks uses sensor measurements to update a model of an unknown or estimated process, which in turn guides the collection of subsequent measurements—a feedback control framework called adaptive sampling. Applications for adaptive sampling exist in a wide range of settings, especially for unmanned or autonomous vehicles that can be deployed cheaply and in cooperative groups. The dynamics of mobile sensor platforms are often simplified to planar self-propelled particles subject to the ambient flow of the surrounding fluid. Sensor measurements are assimilated into continuous or discrete models of the process of interest, which in general can vary in space and time. The variability of the estimated process is one metric to score future candidate sampling trajectories, along with information- and uncertainty-based metrics. Sampling tasks are allocated to the network using centralized or decentralized optimization, in order to avoid redundant measurements and observational gaps.

A multi-parameter physical fluid sensor system for industrial and automotive applications

An online condition monitoring system based on the measurement of viscosity and density of liquids is presented and applied to three different measuring tasks relevant for industrial and automotive applications. One topic is oil characterization in hydraulic systems. It is shown that by measuring over varying temperature and pressure, additional physical properties can be made available for online condition monitoring, which are difficult to measure otherwise. These include, for example, the coefficient of thermal expansion and the bulk modulus, which is also related to the proportion of dissolved air. In the second application we investigate the efficiency of a passive oil defoamer and estimate the percentage of free air. Finally, the suitability of the measurement system for the determination of the diesel fraction in the engine oil as caused by the regeneration cycles of the diesel particulate filter is demonstrated.

The Influence of Incident Power on the Magnetic Fluid Sensor Sensitivity Based on Optical Transmission Properties

Sensitivity is a critical characteristic of sensors, and increasing the sensitivity of the sensor is valuable for measurement study. Incident power has an important influence on the sensitivity of magnetic fluid sensors based on optical transmission properties. Variation in the magnetic field sensitivity at different incident powers was investigated by the measurement of transmitted power through the magnetic fluid sensors. As the magnetic field strength increases, the sensitivity variation of the magnetic fluid film sensor can be divided into four stages: first decreasing sharply, secondly increasing, then decreasing gradually, and finally tending toward a small stable value. The magnitudes of the change in the sensor sensitivity are influenced by the incident power, because the structural pattern of the nano-magnetic particles in the magnetic fluid sensor changes, the Soret effect and the Photonic Hall effect co-define the sensing system. In the weak magnetic field range, when a higher sensitivity is required, it is appropriate to select a larger incident power; however, in a large magnetic field range, when a higher sensitivity is required, a small incident power should be selected. Therefore, the magnetic fluid film sensor exhibits different sensitivity characteristics if different incident power values are chosen. The appropriate incident power can be selected according to the range of the magnetic field to be measured to improve the sensitivity in the magnetic field measurement study.

Accuracy of flash glucose monitoring and continuous glucose monitoring technologies: Implications for clinical practice

Continuous glucose monitoring and flash glucose monitoring technologies measure glucose in the interstitial fluid and are increasingly used in diabetes care. Their accuracy, key to effective glycaemic management, is usually measured using the mean absolute relative difference of the interstitial fluid sensor compared to reference blood glucose readings. However, mean absolute relative difference is not standardised and has limitations. This review aims to provide a consensus opinion on assessing accuracy of interstitial fluid glucose sensing technologies. Mean absolute relative difference is influenced by glucose distribution and rate of change; hence, we express caution on the reliability of comparing mean absolute relative difference data from different study systems and conditions. We also review the pitfalls associated with mean absolute relative difference at different glucose levels and explore additional ways of assessing accuracy of interstitial fluid devices. Importantly, much data indicate that current practice of assessing accuracy of different systems based on individualised mean absolute relative difference results has limitations, which have potential clinical implications. Healthcare professionals must understand the factors that influence mean absolute relative difference as a metric for accuracy and look at additional assessments, such as consensus error grid analysis, when evaluating continuous glucose monitoring and flash glucose monitoring systems in diabetes care. This in turn will ensure that management decisions based on interstitial fluid sensor data are both effective and safe.